Transformer Research Articles

Effects of quenching on domain switching and electric field-induced phase transformations in Na0.5Bi0.5TiO3-NaNbO3 ceramics

Quenching from high temperatures has been identified as a useful means to enhance the piezoelectric properties and thermal stability of bismuth-based perovskite ferroelectrics. In the present work, it is demonstrated that quenching leads to improvement of depolarization temperature, ferroelectric and piezoelectric properties in Na0.5Bi0.5TiO3-NaNbO3 (NBT-0.1NN) ceramics. In-situ synchrotron x-ray diffraction measurements indicated an irreversible transformation from cubic to coexisting cubic and rhombohedral phases during the application of a high electric field, for both as-sintered and quenched ceramics. These results confirm the non-ergodic relaxor ferroelectric nature of the materials. DC poling induced a transformation to single-phase rhombohedral structure in both cases, with highly textured domain configurations. These well-oriented ferroelectric domain states were relatively stable under subsequent bipolar electric field cycling. For the pre-poled NBT-0.1NN ceramics, the quenched samples were found to exhibit the highest intrinsic (lattice strain) and extrinsic (domain switching) contributions to electrostrain, due to the increased rhombohedral distortion.

Exploring Factors Influencing the Internal Displacement Decisions Amidst Natural Disasters in Bangladesh

Bangladesh, a densely populated country in the world’s largest coastal delta, is seriously threatened by the ongoing onslaught of major natural disasters brought on by climate change. The main perpetrators, cyclonic storms and floods cause millions of displaced people each year and increase the vulnerability of communities already facing devastation from the environment and poverty due to these disasters. This study explores the complex patterns of internal displacement brought on by cyclonic storms and floods in Bangladesh from 2008 to 2022. The study identifies the patterns, trends, vulnerable districts, and influencing factors of displacement by analysing data from multiple sources. Every year, an average of 6.14 million people are displaced by floods, a persistent problem. Gaibandha is the district most at risk, having seen 13 floods in the previous 15 years. Throughout the study period, Bhola experienced eight strikes from cyclonic storms that decimated the southern coast. These natural disasters force people to move from environmentally susceptible to safe areas. The disaster’s intensity or frequency and some economic, social, and political factors influence this decision to displace. The study also identifies factors that influence displacement decisions after catastrophic disasters. Additionally, it emphasises the importance of understanding displacement patterns and influencing factors to make informed decisions about displacement in such situations.

Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers

Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers

Dependability Analysis of the Negative-Sequence Turn-to-Turn Fault Protection Schemes for MMC-HVDC Converter Transformers

This paper addresses two complex areas of research: the detection of turn-to-turn faults (TTF) in power transformers and the impact of inverter-based resources on the TTF protection scheme operation. Detecting turn-to-turn faults in power transformers by protection algorithms poses a challenge due to the minimal fault currents observed at transformer terminals. Yet, the demand for dependable TTF protection is very high because of the high fault currents inside the shorted turns and the resulting damage consequences. On the other hand, for such sensitive protection, adverse conditions such as transformer inrush currents or CT errors may lead to protection maloperation. Moreover, the fault current characteristic of the inverter-based source infeed is very different compared to the synchronous machine infeed, particularly concerning the negative-sequence current used in the TTF protection schemes, which calls for thorough research analysis. A simulation model of the converter transformer capable of simulating TTFs, and the Modular Multilevel Converter (MMC) for a High Voltage Direct Current (HVDC) transmission link, has been developed. The test results of turn-to-turn fault protection schemes in inverter-based generation-dominated power systems compared to the synchronous generator infeed are presented. The negative-sequence current protection quantities are analysed in more detail for TTFs with small and large number of shorted turns, i.e. without and with reactive negative-sequence current injection by the MMC control. Finally, the paper assesses the dependability of the transformer differential protection and sensitive TTF protection schemes in detecting faults with different numbers of shorted turns and fault resistance for TTFs occurring in the star and delta winding of the converter transformer.

Transformer oil temperature sensing utilizing bundle plastic optical fiber sensor

A bundle plastic optical fiber (POF) that works based on an intensity modulation technique is experimentally demonstrated to sense the temperature of transformer oil. The sensor was developed using a bundle POF that is located perpendicular to an aluminum reflective film with an airgap cavity between these two elements. The simplicity of the architecture allows the development of an economical optical sensor system. To avoid interference effects by other substances in the oil, the sensor head is encapsulated with a metal protecting tube. The temperature measurement was realized in this study by monitoring the output light intensity in the visible light spectrum. For linearity range from 40 °C to 75 °C, the tested sensor exhibits a sensitivity of 0.0064 °C−1, a linearity coefficient of 0.95 and a resolution of 1.56 °C. These results demonstrate the suitability of the developed sensor for temperature oil monitoring in an electrical power transformer system.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

An Isolated High Gain Push Pull Type DC to DC Converter for Fuel Cell Power Generation Systems

Due to their high efficiency and low environmental impact, fuel cell power generation systems have emerged as promising alternatives to traditional energy sources. However, the integration of fuel cells into practical applications necessitates the development of efficient power conversion systems. dc-dc converters play a crucial role in optimizing the power flow between the fuel cell stack and the load. In this paper, a push-pull type high gain dc-dc converter is proposed which consists of a push-pull inverter, a rectifier circuit, and C-filter. The push pull inverter produces high frequency square wave output while the full bridge rectifier and C-filter convert this square wave output into desired dc voltage. The converter boosts the 12V dc input supply to 326 V, representing a gain factor of more than 27. The isolated coupling inductor used in push pull inverter produces high gain and provides electrical isolation. Besides, the proposed topology eliminates the use of bulky grid-connected power transformers that decreases the system size and cost. To evaluate the performance of the proposed topology, a number of simulations under varying loads were carried out in the MATLAB Simulink framework. Further-more, a scaled-down prototype of 160W at 12/326 V was developed to confirm the simulation results. The findings indicate that the suggested converter can be a viable option for the hydrogen fuel cell-based power conversion system. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 37-52

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

AbstractInsulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

Secondary Order RC Sensor Neuron Circuit for Direct Input Encoding in Spiking Neural Network

Secondary Order RC Sensor Neuron Circuit for Direct Input Encoding in Spiking Neural Network

Weakly Supervised Transformer for Radar Jamming Recognition

Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition performance. Additionally, recognition performance is hindered by limitations in capturing the global features of radar jamming. The Transformer (TR) has advantages in modeling long-range relationships. Therefore, a weakly supervised Transformer is proposed to address the issues of performance degradation under weak supervision. Specifically, complementary label (CL) TR, called RadarCL-TR, is proposed to improve radar jamming recognition accuracy with noisy samples. CL learning and a cleansing module are successively utilized to detect and remove potentially noisy samples. Thus, the adverse influence of noisy samples is mitigated. Additionally, semi-supervised learning (SSL) TR, called RadarSSL-PL-TR, is proposed to boost recognition performance under unlabeled samples via pseudo labels (PLs). Network generalization is improved by training with pseudo-labeling unlabeled samples. Moreover, the RadarSSL-PL-S-TR is proposed to further promote recognition performance, where a selection module identifies reliable pseudo-labeling samples. The experimental results show that the proposed RadarCL-TR and RadarSSL-PL-S-TR outperform comparison methods in recognition accuracy by at least 7.07% and 6.17% with noisy and unlabeled samples, respectively.

Localization for Dual Partial Discharge Sources in Transformer Oil Using Pressure-Balanced Fiber-Optic Ultrasonic Sensor Array.

The power transformer is one of the most crucial pieces of high-voltage equipment in the power system, and its stable operation is crucial to the reliability of power transmission. Partial discharge (PD) is a key factor leading to the degradation and failure of the insulation performance of power transformers. Therefore, online monitoring of partial discharge can not only obtain real-time information on the operating status of the equipment but also effectively predict the remaining service life of the transformer. Meanwhile, accurate localization of partial discharge sources can assist maintenance personnel in developing more precise and efficient maintenance plans, ensuring the stable operation of the power system. Dual partial discharge sources in transformer oil represent a more complex fault type, and piezoelectric transducers installed outside the transformer oil tank often fail to accurately capture such discharge waveforms. Additionally, the sensitivity of the built-in F-P sensors can decrease when installed deep within the oil tank due to the influence of oil pressure on its sensing diaphragm, resulting in an inability to accurately detect dual partial discharge sources in transformer oil. To address the impact of oil pressure on sensor sensitivity and achieve the detection of dual partial discharge sources under high-voltage conditions in transformers, this paper proposes an optical fiber ultrasonic sensor with a pressure-balancing structure. This sensor can adapt to changes in oil pressure environments inside transformers, has strong electromagnetic interference resistance, and can be installed deep within the oil tank to detect dual partial discharge sources. In this study, a dual PD detection system based on this sensor array is developed, employing a cross-positioning algorithm to achieve detection and localization of dual partial discharge sources in transformer oil. When applied to a 35 kV single-phase transformer for dual partial discharge source detection in different regions, the sensor array exhibits good sensitivity under high oil pressure conditions, enabling the detection and localization of dual partial discharge sources in oil and winding interturn without obstruction. For fault regions with obstructions, such as within the oil channel of the transformer winding, the sensor exhibits the capability to detect the discharge waveform stemming from dual partial discharge sources. Overall, the sensor demonstrates good sensitivity and directional clarity, providing effective detection of dual PD sources generated inside transformers.

Transformer Differential Protection Method for Recognition between Power Transformer Internal Defects and Inrush Current: A Comprehensive Review of Detection Techniques

The cornerstone of any electric power system lies in its power transformers, as their seamless operation is crucial for network reliability. Instant disconnection from the grid is imperative upon detecting any faults to prevent cascading issues. However, distinguishing between fault conditions, like inrush current, which necessitates caution rather than immediate action, poses a challenge for effective protection schemes. This dilemma can lead to relay malfunctions, further jeopardizing system integrity. This paper delves into a thorough analysis and comparison of various methods employed in differential protection to discern between internal faults and inrush currents, aiming to enhance system resilience. This comprehensive review explores the efficacy of intelligent techniques, hybrid approaches, and traditional methods in fault detection. By shedding light on the strengths and limitations of each method, researchers in this domain can glean insights to innovate and address the deficiencies of existing strategies in tackling internal faults and inrush currents detection. Ultimately, this endeavor seeks to fortify the reliability and stability of power systems in the face of dynamic operational challenges.

Evaluating the insulation condition of oil‐impregnated paper bushings based on the time constant database

AbstractAssessing the insulation condition of oil‐impregnated paper (OIP) bushings is necessary to guarantee the safe operation of power transformers. Moisture is the main threat to the insulation of OIP bushings. Therefore, accurately estimating their moisture value is crucial to keep power systems running smoothly. However, it has been suggested that the ageing status of OIP bushings should be considered while assessing the moisture. Dielectric frequency‐domain spectroscopy (FDS) has been extensively employed for assessing insulation condition of OIP insulations due to its benefits. A new approach was proposed to assess the insulation condition of OIP bushings using FDS results. Various OIP samples with various moisture levels and degrees of polymerisation (DP) were selected. The extended Debye model (EDM) parameters related to each sample were calculated using their tanδ − f curve. A time constant database (TCD) was created using the obtained time constants of EDM, which includes the dominant time constant and time constant related to the R0 – C0 branch, and their corresponding insulation condition. Then, a method was introduced based on the created TCD to estimate the insulation condition of OIP bushings. The results showed that the average relative error of moisture and DP estimation is 8.33% and 6.32%, respectively.

Application of saturated core transformer as inrush current limiter

An inrush current is a transient current that results from a sudden change in the exciting voltage across a transformer's windings. Reducing the inrush current of transformers is desirable due to mechanical stresses and negligent operation of protective relay systems such as differential relays. This paper investigates the application of a saturated-core transformer as an inrush current limiter (ICL). Analytical methods of inrush current and its electromagnetic forces on transformer windings with and without application of proposed ICL are presented. Also, the core saturation of the ICL and transformer, in addition to the voltage drop on the ICL in transient and steady states, are investigated. A single-phase transformer is modeled in two dimensions (2-D), and the forces due to inrush current are calculated by the finite element method (FEM) in Maxwell software. The radial and axial force distributions of transformer windings with and without the ICL are investigated. The results show the capability of ICL to limit the transformer inrush current and reduction of its electromagnetic forces. Our simulations demonstrate a significant reduction in inrush current (75 %) using the proposed Inrush Current Limiter (ICL). Additionally, the ICL effectively mitigates electromagnetic forces within the transformer. Compared to a scenario without ICL, the ICL reduces radial forces by 80 % and axial forces by a substantial 90 %. These findings suggest that the ICL offers a promising solution for mitigating both inrush current and the associated electromagnetic forces in power transformers.

Optimal design of circulating water system in pressurized water reactor nuclear power plant

The optimization of the circulating water system of pressurized water reactor nuclear power plants can effectively improve the profit without affecting the primary circuit operation, which is of great significance in enhancing the market competitiveness of nuclear power plants. The circulating water system of the nuclear power plant is modeled, and the circulating water system optimization scheme is formed after the nuclear power unit is reformed from three aspects: condenser heat transfer area, condenser connection mode, and circulating water pump operation mode. The circulating water system optimization calculation is carried out for each scheme by using the minimum annual cost method. Based on the sensitivity analysis of the factors that affect the profits of nuclear power plants, the best circulating water system optimization scheme is finally given for new nuclear power plants and nuclear power plants in operation, which provides a reference for the construction of related nuclear power projects.

Large dump critical zone of interest identification and slope failure prediction using realistic 3D numerical modelling

Dump failure is one of the most catastrophic hazards in the opencast mining industry and involves sudden and violent failures. Therefore, susceptible region identification is essential for slope failure mitigation in the large dump. This study proposed a sustainable approach for large dump critical zone of interest (CZI) identification, stability monitoring and slope failure prediction. UAV close-range photogrammetry survey and structure-from-motion (SfM) approach were used for the dump realistic 3D model reconstruction. 3D numerical modelling was employed for dump stability analysis. In this investigation, the dump critical zone, slope factor of safety (FOS), displacement and slope failure prediction were analysed using the 3D numerical modelling. The results indicate that maximum displacement was found in the critical zone I. However, the dump was stable under static conditions. Dump critical slope failure time is predicted using the inverse velocity method. The results of this investigation are reliable and establish the scope of UAV photogrammetry for realistic 3D modelling. In addition, the slope stability state and failure model were consistent with field observations. The proposed approach can be used for the mine, hill, and roadcut slopes. UAV technology and 3D modelling approaches have substantially reduced data collection time and expense.

Seismic Response Prediction of Porcelain Transformer Bushing Using Hybrid Metaheuristic and Machine Learning Techniques: A Comparative Study

Although seismic response predictions are widely used for engineering structures, their applications in electrical equipment are rare. Overstressing at the bottom of the porcelain insulators during seismic events has made power transformer bushings in substations prone to failure. Thus, this paper proposed and compared six integrated machine learning (ML) models for seismic stress response predictions for porcelain transformer bushings using easily monitored acceleration responses. Metaheuristic algorithms such as particle swarm optimization were employed for architecture tuning. Prediction accuracies for stress response values and classifications were evaluated. Finally, shaking table tests and simulation analyses for a 1100 kV bushing were implemented to validate the accuracy of the six ML models. The results indicated that the proposed ML models can quickly forecast the maximum stress experienced by a porcelain bushing during earthquakes. Swarm intelligence evolutionary technologies could quickly and automatically aid in the retrofitting of architecture for the ML models. The K-nearest neighbor regression model had the best level of prediction accuracy among the six selected ML models for experimental and simulation validations. ML prediction models have clear benefits over frequently used seismic analytical techniques in terms of speed and accuracy for post-earthquake emergency relief in substations.

Solvent-free synthesis of nickel doped ruthenium for efficient hydrogen evolution

The efficient transformation of chemicals and electricity via electrocatalytic hydrogen evolution reaction (HER) plays a pivotal role in green hydrogen production. Both in academia and industry, the pursuit of designing and fabricating catalysts for HER that are not only efficient and robust but also environmentally friendly is of significant interest. In this work, a ruthenium catalyst doped with nickel was synthesized using a procedure-minimal and solvent-free method facilitated by microwave-assisted pyrolysis. The incorporation of alloying and optimized electronic structures has resulted in Ru-based nanocrystals that change the HER mechanism and enable enhanced activity. The optimal RuNi/C catalyst demonstrates an overpotential of only 15 mV at a current density of 10 mA cm−2, with a Tafel slope of 24 mV dec−1, surpassing the performance of the state-of-the-art Pt/C catalyst. Remarkably, the prepared catalyst exhibits admirable corrosion resistance, making it a highly promising candidate for green hydrogen production through seawater splitting.

Effect of anions on formation of ferrite coatings on carbon steel by hydrothermal method

The major concerns in Primary Heat Transport circuits of water cooled nuclear power plants are corrosion of structural materials and radiation field buildup on out-of-core surfaces due to accumulation of activated corrosion products. To address these concerns, hot conditioning and metal ion passivation are employed, which lead to the formation of protective magnetite coating on structural alloys like carbon steel. In this study, investigations were undertaken to modify the magnetite/ferrite films hydrothermally in the presence of zinc (Zn2+) and nickel (Ni2+) ions by using different salts of these ions containing anions such as acetate, nitrate and sulphate. Mixed ferrite coatings containing Zn2+ and Ni2+ ions were developed on carbon steel in lithiated water in presence of these anions at 250 °C by hydrothermal method and subsequently characterized by grazing incidence-X-ray diffraction for phase identification and scanning electron microscopy for surface morphology. The effects of these anions were studied by comparing the corrosion resistance properties of ferrite coated carbon steel specimens. The corrosion properties were studied in lithium hydroxide medium at ambient temperature by electrochemical techniques and the results of the investigation are discussed in this paper.

Fluctuation of fine motor skills throughout the menstrual cycle in women

The effect of the menstrual cycle on fine motor skills is unclear. This study determined whether the menstrual cycle affected fine motor skills and related neural activities. Nineteen women with regular menstrual cycles were tested for fine motor skills using two types of tasks: grooved pegboard task (GPT), which evaluates motor control with high freedom of movements, and force modulation task (FMT), which evaluates more complex and fine motor control with low freedom of movements. We also assessed primary motor cortex intracortical circuits and sensorimotor integration using paired-pulse transcranial magnetic stimulation to reveal why the menstrual cycle affects fine motor skills. The present study indicated that fine motor skills assessed by FMT varied throughout the menstrual cycle while those measured by GPT did not. These results suggest that fine motor skills requiring more complex and fine control may be affected by the menstrual cycle. Additionally, changes in fine motor skills throughout the menstrual cycle may be associated with the severity of menstruation-related symptoms.